Your browser doesn't support javascript.
loading
Antioxidant activity prediction and classification of some teas using artificial neural networks.
Cimpoiu, Claudia; Cristea, Vasile-Mircea; Hosu, Anamaria; Sandru, Mihaela; Seserman, Liana.
Afiliação
  • Cimpoiu C; "Babes-Bolyai" University, Faculty of Chemistry and Chemical Engineering, 11 Arany Janos, 400082 Cluj-Napoca, Romania. Electronic address: ccimpoiu@chem.ubbcluj.ro.
  • Cristea VM; "Babes-Bolyai" University, Faculty of Chemistry and Chemical Engineering, 11 Arany Janos, 400082 Cluj-Napoca, Romania.
  • Hosu A; "Babes-Bolyai" University, Faculty of Chemistry and Chemical Engineering, 11 Arany Janos, 400082 Cluj-Napoca, Romania.
  • Sandru M; "Babes-Bolyai" University, Faculty of Chemistry and Chemical Engineering, 11 Arany Janos, 400082 Cluj-Napoca, Romania.
  • Seserman L; "Babes-Bolyai" University, Faculty of Chemistry and Chemical Engineering, 11 Arany Janos, 400082 Cluj-Napoca, Romania.
Food Chem ; 127(3): 1323-8, 2011 Aug 01.
Article em En | MEDLINE | ID: mdl-25214133
ABSTRACT
In order to characterise and to classify some teas a simple, rapid and economical method based on composition, antioxidant activity and artificial neural networks (ANNs) is proposed. For these purpose two types of ANN based applications have been developed one for predicting the antioxidant activity and a second one for establishing the class of the teas. The complex relationship between the total antioxidant activity (AA) depending on the total flavonoids content (F), total catechins content (C) and total methyl-xanthines content (MX) of commercial teas was revealed by the first designed feed-forward ANN. Secondly, using a probabilistic ANN, successful tea classification in various classes (green tea, black tea and express black tea) was also performed.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2011 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2011 Tipo de documento: Article